The present invention is concerned with the problem of controlling artificial limbs in cases where several limb functions require control. These situations are of importance especially to above-elbow upper extremity amputees. Here, classical EMG (myoelectric) controllers have failed in the past, since they were based only on determining the existence or non-existence of an EMG signal. Recent work performed by others has approached the multifunctional control problem by using a large number of electrodes, though still considering only a limited part of the EMG spectrum. The approach employed in the present invention is based on that described in U.S. Pat. No. 4,030,141 to Daniel Graupe, employing consideration of the whole spectrum of the EMG signal via identifying its time series model. However, the present invention subsequently employs parallel filtering to discriminate between the various limb functions of interest to achieve fast discrimination and control, as required for practical applications, since this allows the basic identification procedure itself to be performed off line.
Multifunctional control of artificial upper extremities via using myoelectric (EMG) signals is of major importance in the above-mentioned cases of above-elbow amputees. For solving this problem it is essential to distinguish between the different limb functions to be controlled from the pattern of the myoelectric signal at some or several stump locations. Hence, differences in pattern of myoelectric signals related to various limb functions (i.e., elbow bending, elbow extension, wrist pronation, wrist supination, grasp, etc.), as taken at one or several stump muscles, must be detected. Although such differences do exist, they are hardly obvious to the naked eye of even an expert. Two major approaches to solve this problem have been suggested. One, based on the works of Lawrence (1) and of Lyman et al (2), requires mapping of many (ten or more) electrode locations at each of which the EMG function is strongly correlated with a single limb function. It thus employs the low frequency characteristics of the EMG signals and of their mapping or distribution over the various electrode locations. Furthermore, due to the number of electrode sites required, this method is limited to amputees with little nerve and muscle damage to their stumps and with relatively long stumps. The other approach, based on the work of Graupe et al (3), (4), requires a far smaller number of electrode locations (one to three) since it aims at recognition at locations where even very weak correlations between the measured signal and (more than one) limb functions may exist. This method takes advantage of cross-talk between signals relating to different limb functions, in contrast to other methods where this cross-talk is gotten rid of and is disturbing. This method is thus suitable for amputees with severe nerve and muscle damage in their stumps. Moreover, this method is concerned with the complete spectrum since it implies that the complete linear information content of the EMG signal is considered (i.e., at all frequencies. It is thus more efficient in terms of utilizing the information of the EMG signal, such that less electrode locations need be considered, though at the price of requiring finer detection. It is to be noted that the above correlation with more than one limb function is due to the spatial integration effect of muscle fiber and skin tissue that affects the signal as measured by surface electrodes (5).
The following publications, to which reference by number is made herein, appear to show the present state of the art:
1. Lawrence, P. "Computer Design and Simulation of a Myoelectric System for Controlling a Multifunctional Prosthetic Hand". Proc. Symp. on Pattern Recognition and Mathematical Image Processing. Chalmers University, Goeteborg, Sweden, August, 1972.
2. Lyman, T., Freedy, A., and Solomonow, M., "Studies Toward a Practical Computer-Aided Arm Prosthesis System". Bull. Prosth. Res. pp 213-225, Fall Issue, 1974.
3. Graupe, D. and Cline, W. K., "Functional Separation of EMG Signals via ARMA Identification Methods for Prosthetic Control Purposes". IEEE Trans. on Systems, Man and Cybernetics. Vol. SMC-5, pp. 252-259, March 1975.
4. Graupe, D., "Control of Upper Limb Prosthesis in Several Degrees of Freedom". Bull. Prosth. Res. pp 226-236, Fall Issue, 1974.
5. Brody, G., Balasubramanian, R., and Scott, R. N., "A Model for Myoelectric Signal Generator", Med. & Bio. Eng. pp 29-41, Jan. 1974.
6. Graupe, D., "Identification of Systems", 2nd Edition, R. E. Krieger Publishing Co., New York, N.Y., 1976.
7. Rasch and Burke, "Kinesiology and Applied Anatomy", 5th Edition, H. Kimpton Publishers, London, (p.55)
8. Papoulis, A., "Probability, Random Variables, and Stochastic Processes", McGraw Hill, New York, N.Y., 1965, (Chapter 16).
9. Graupe, D., Krause, D. J., and Moore, J. B., "Identification of Auto-regressive Moving-Average Parameters of Time Series", IEEE Trans. on Aut. Cont., Vol. AC-20, pp 104-107, Feb. 1975.
10. Saradis, G. N., "Comparison of Five Popular Identification Algorithms--A Survey", Proc. Decision and Control Conf., New Orleans, 1972.
11. Luenberger, D. G., "Optimization by Vector Space Methods", John Wiley and Sons, New York, N.Y., 1969.
12. Datel Systems, Inc. "Engineering Product Handbook", No. AT 75408.
13. Intel Corp., "Intel 8080 Microcomputer Systems Manual", 1975.
14. Graupe, D., U.S. Pat. No. 4,030,141.